Data and usage in self-service belong together. Self-service systems now generate large volumes of data. They capture transactions, waiting times, interventions, aborted processes, error messages, utilisation and many other events during day-to-day operation.
But data alone does not make a self-service system better.
What matters is whether this information is available, interpreted correctly and translated into concrete decisions. Just as important is whether customers and employees actually understand the system and can use it confidently.
This is exactly where Levels 3 and 4 of the Multi-Level Approach come in (see Retail Engineering: Rethinking Self-Service):
After the fundamental decision in favour of self-service and the structural design of the self-service zone (see Retail Engineering: How an MLA Project Starts), the next step is to make the system transparent, measurable and continuously improvable in day-to-day operation.
Level 3: Data in Self-Service – Network & Analytics
A modern self-checkout is not an isolated device.
It is part of a connected system landscape. POS systems, product data, payment, scales, age verification, security functions, monitoring and other applications all need to communicate reliably with one another (see also Diebold Nixdorf: Checkout of the Future).
Only when these systems work together properly can a reliable data foundation be created.
The logical chain is:
Connectivity → Data → Analysis → Management.
The objective is not to collect as much data as possible. What matters is making the information available that is actually relevant to day-to-day operation.
For example:
- What is the usage rate?
- How long do customers wait?
- How often do employees need to intervene?
- At which points are transactions abandoned?
- Which error messages occur particularly frequently?
- Which products or process steps regularly cause problems?
- How does throughput change over the course of the day?
- Where do unusual patterns or anomalies occur?
Only when this information is available promptly and meaningfully connected can operational decisions be derived from it.
A dashboard alone does not create transparency. What matters is whether the values displayed help identify specific causes and improve processes.
Level 4: Usage in self-service | Usability & customer experience
But even the best data architecture does not explain why a particular value occurs.
A high intervention rate initially tells us only that employees frequently need to step in.
It does not yet explain why.
Perhaps an instruction on the screen is unclear. Or feedback is provided too late. A process step may be unnecessarily complicated. It may also simply be the case that customers do not know what they are expected to do next.
This is where the fourth MLA level comes into play: Usability & Customer Experience.
The focus is not just on the interface of a terminal, but on the entire user journey.
Does the customer always understand what to do next?
Is system feedback clear?
Can errors be corrected easily?
Does the process remain understandable under time pressure?
Can different user groups operate the solution confidently?
And can employees provide quick, targeted support when assistance is required?
Self-service only works economically if people are both able and willing to use the system.
If customers hesitate, abandon the process or regularly need help, friction, longer waiting times and additional effort arise.
And if employees have to intervene continuously, the workload does not disappear.
It simply shifts elsewhere.
Bringing data and usage in self-service together
This is exactly where Levels 3 and 4 of the MLA interact.
Network & Analytics shows where a process works – and where it does not.
Usability & Customer Experience helps explain why.
This connection is crucial.
An abort rate may indicate a problem. But only observing and analysing the actual user journey can reveal whether the cause is, for example, unclear operating logic, a poorly understood message or an unfavourable process step.
The same applies to waiting times, interventions, operating errors or low usage.
Metrics provide indications (see Retail Engineering: Self-Service Needs to Be Understood).
Actual usage provides context.
Only together do they create the basis for targeted improvements.
Measuring is not enough. Understanding is not enough either.
A self-service system can work perfectly from a technical point of view and still fail in actual use.
Conversely, a well-designed user interface can lose its effectiveness if data is missing, interfaces are unreliable or operational problems remain invisible.
That is why the Multi-Level Approach considers both perspectives together.
It is not only about installing systems.
It is about understanding them in day-to-day operation, managing them and continuously improving them.
Can we measure and manage the system?
And do we understand how people actually use it?
Technology creates possibilities.
Only usage turns them into a working system.
The next MLA article will focus on risks, manipulation and shrinkage – and on why Fraud Detection should be part of a self-service concept from the very beginning.
